Introduction to R - GVPT Maths Boot Camp

Learning objectives for today

Create your first plot in R
Test your hypotheses using informative data visualisations

The Research Process

Source: R4DS

R basics

R code:

1 + 2

[1] 3

Functions:

sum(1, 2)

[1] 3

R packages

Packages are collections of R functions, data, and compiled code in a well-defined format

# Install the relevant package(s)
install.packages("tidyverse")

# Load the package in current session
library(tidyverse)

Data visualisation

From R4DS - Data Visualization:

Do cars with big engines use more fuel than cars with small engines?

Skipping to the end

Load relevant packages and data

# Load the relevant functions
library(tidyverse)

# Load the data
mpg

manufacturer	model	displ	year	cyl
audi	a4	1.8	1999	4
audi	a4	1.8	1999	4
audi	a4	2.0	2008	4
audi	a4	2.0	2008	4
audi	a4	2.8	1999	6
audi	a4	2.8	1999	6

Learning more about the data

To get help on any function or dataset use: ?function

For example, to learn more about this package type ?mpg into your console.

Plot your data

library(ggplot2)

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy))

The plot shows a negative relationship between engine size (displ) and fuel efficiency (hwy). In other words, cars with big engines use more fuel. Does this confirm or refute your hypothesis about fuel efficiency and engine size?

With ggplot2, you begin a plot with the function ggplot(). ggplot() creates a coordinate system that you can add layers to. The first argument of ggplot() is the dataset to use in the graph. So ggplot(data = mpg) creates an empty graph, but it’s not very interesting so I’m not going to show it here.

You complete your graph by adding one or more layers to ggplot(). The function geom_point() adds a layer of points to your plot, which creates a scatterplot. ggplot2 comes with many geom functions that each add a different type of layer to a plot. You’ll learn a whole bunch of them throughout this chapter.

Each geom function in ggplot2 takes a mapping argument. This defines how variables in your dataset are mapped to visual properties. The mapping argument is always paired with aes(), and the x and y arguments of aes() specify which variables to map to the x and y axes. ggplot2 looks for the mapped variables in the data argument, in this case, mpg.

EXERCISES

What do you see when you run the following?

ggplot(data = mpg)

How many rows are in mpg? How many columns?

nrow(mpg)
ncol(mpg)

What does the drv variable describe?

?mpg

Make a scatterplot of hwy vs cyl.

What happens if you make a scatterplot of class vs drv? Why is the plot not useful?

Let’s look at groups in the data

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy, color = class))

Let’s look at groups in the data

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy, size = class))

Let’s look at groups in the data

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy, shape = class))

Let’s make this prettier

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy), color = "blue")

Let’s add useful headings

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy), color = "blue") + 
  labs(
    title = "Relationship between engine displacement and highway miles per gallon",
    x = "Engine displacement (L)",
    y = "Highway miles per gallon"
  )

Let’s add useful headings

Let’s clean this up

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy), color = "blue") + 
  theme_minimal() + 
  labs(
    title = "Relationship between engine displacement and highway miles per gallon",
    x = "Engine displacement (L)",
    y = "Highway miles per gallon"
  )

Let’s clean this up

Creating your own theme

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy, color = class)) + 
  theme(
    legend.position = "bottom",
    panel.grid = element_blank(),
    panel.background = element_blank(),
    plot.title.position = "plot",
    plot.title = element_text(face = "bold")
  ) + 
  labs(
    title = "Relationship between engine displacement and highway miles per gallon by class",
    x = "Engine displacement (L)",
    y = "Highway miles per gallon",
    color = "Class"
  )

Creating your own theme

EXERCISES

What’s gone wrong with this code? Why are the points not blue?

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy, color = "blue"))

EXERCISES

Which variables in mpg are categorical? Which variables are continuous?

Map a continuous variable to color, size, and shape. How do these aesthetics behave differently for categorical vs. continuous variables?

What happens if you map the same variable to multiple aesthetics?

EXERCISES

What does the stroke aesthetic do? What shapes does it work with?

What happens if you map an aesthetic to something other than a variable name, like aes(colour = displ < 5)? Note, you’ll also need to specify x and y.

Looking at group-specific trends

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy)) + 
  facet_wrap(~ class, nrow = 2)

Looking at group-specific trends

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy)) + 
  facet_grid(drv ~ cyl)

Summarising relationships in the data

ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) + 
  geom_point() + 
  geom_smooth(method = "lm", se = F)

Group-specific relationships

ggplot(data = mpg, mapping = aes(x = displ, y = hwy, color = drv)) + 
  geom_point() + 
  geom_smooth(method = "lm", se = F) + 
  theme_minimal()

Summary

Today you:

Set up your data science tools
Plotted complex data in an engaging way
Discovered interesting relationships in the data
Connected these relationships or trends to your expectations (or hypotheses about the data)